DKP-SLAM:A Visual SLAM for Dynamic Indoor Scenes Based on Object Detection and Region Probability  

作　　者：Menglin Yin Yong Qin Jiansheng Peng 

机构地区：[1]College of Automation,Guangxi University of Science and Technology,Liuzhou,545000,China [2]Department of Artificial Intelligence and Manufacturing,Hechi University,Hechi,546300,China [3]Key Laboratory of AI and Information Processing,Education Department of Guangxi Zhuang Autonomous Region,Hechi,546300,China [4]Guangxi Key Laboratory of Sericulture Ecology and Applied Intelligent Technology,School of Chemistry and Bioengineering,Hechi University,Hechi,546300,China

出　　处：《Computers, Materials & Continua》2025年第1期1329-1347,共19页计算机、材料和连续体（英文）

基　　金：the National Natural Science Foundation of China(No.62063006);to the Guangxi Natural Science Foundation under Grant(Nos.2023GXNSFAA026025,AA24010001);to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2023RY018);to the Special Guangxi Industry and Information Technology Department,Textile and Pharmaceutical Division(ID:2021 No.231);to the Special Research Project of Hechi University(ID:2021GCC028);to the Key Laboratory of AI and Information Processing,Education Department of Guangxi Zhuang Autonomous Region(Hechi University),No.2024GXZDSY009。

摘　　要：In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.

关 键 词：Visual SLAM dynamic scene YOLOX K-means++clustering dynamic probability 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]